querySoQL query.
| Name | Required | Description | Default |
|---|---|---|---|
| group | No | ||
| limit | No | ||
| order | No | ||
| where | No | ||
| offset | No | ||
| select | No | ||
| resource_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes | Array of records matching SoQL query |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "a9u6-brt9"
+ },
+ {
+ "limit": 100,
+ "resource_id": "a9u6-brt9",
+ "select": "case_number, date, location",
+ "where": "year > 2020"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "description": "Array of records matching SoQL query",
+ "items": {
+ "description": "Record from resource matching query criteria",
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which characterize the tool as safe. The description adds no additional behavioral context (e.g., rate limits, pagination, or result format), so it provides minimal value beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
At two words, the description is extremely concise, but it fails to convey essential information. It is under-specified rather than efficient, sacrificing clarity for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters, 0% schema coverage, and an output schema (not shown), the description is woefully incomplete. It does not state the tool's purpose, operation, or expectations for input/output, leaving the agent to infer everything from parameter names and examples.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 0% description coverage for 7 parameters. The description 'SoQL query.' does not explain any parameter meaning, syntax, or expected values. Despite parameter names being somewhat self-explanatory, the agent receives no guidance on how to construct queries, especially for complex parameters like 'where' or 'select'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'SoQL query.' adds minimal specificity beyond the tool name 'query'. It identifies the query language but does not state what the tool does (e.g., execute a query on a dataset and return results). This is slightly better than a tautology but still vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., search, filter). There are no usage conditions, prerequisites, or exclusions, leaving the agent without decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve query/research; polymarket_edges, polymarket_arbitrage, bet_research, and polymarket_edge_tracker all analyze prediction markets; entity_profile, compare_entities, and recent_changes overlap on company research. An agent could easily select the wrong one.
All names are lowercase snake_case, but the verb-noun convention is inconsistent: some are verb-first (ask_pipeworx, generate_llms_txt, validate_claim), some noun-first (entity_profile, polymarket_edges, recent_changes), and some are bare nouns (query, metadata, datasets). The pattern is readable but not uniform.
34 tools is excessive for the apparent scope, especially given the server name suggests a Chicago city-data focus while the vast majority of tools are generic data-research, prediction-market, and memory utilities. This feels like a kitchen-sink bundle rather than a focused, well-scoped toolset.
The tool surface is a disjointed collection covering querying, research, memory, subscriptions, and prediction markets, but it lacks coherent lifecycle coverage for any single domain. For the named 'Cityofchicago' purpose, there is almost no city-specific functionality, and even as a general data tool, obvious gaps remain (e.g., no direct dataset management or update/delete operations).